Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:27128972", "kind": "science-record", "revision": 8, "content_hash": "sha256:d27b2483bc6dfebb3d867df554c9524edf65f088f9ef4b218adad485136025af", "created_at": "2026-11-01T15:40:18Z", "updated_at": "2026-11-01T22:42:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma.", "doi": "10.1371/journal.pone.0154074", "pmid": "27128972", "authors": ["Benjamin A Neely", "Christopher E Wilkins", "Laura A Marlow", "Dariya Malyarenko", "Yunee Kim", "Alexandr Ignatchenko", "Heather Sasinowska", "Maciek Sasinowski", "Julius O Nyalwidhe", "Thomas Kislinger", "John A Copland", "Richard R Drake"], "journal": "PloS one", "year": 2016, "abstract": "Renal cell carcinoma comprises 2 to 3% of malignancies in adults with the most prevalent subtype being clear-cell RCC (ccRCC). This type of cancer is well characterized at the genomic and transcriptomic level and is associated with a loss of VHL that results in stabilization of HIF1. The current study focused on evaluating ccRCC stage dependent changes at the proteome level to provide insight into the molecular pathogenesis of ccRCC progression. To accomplish this, label-free proteomics was used to characterize matched tumor and normal-adjacent tissues from 84 patients with stage I to IV ccRCC. Using pooled samples 1551 proteins were identified, of which 290 were differentially abundant, while 783 proteins were identified using individual samples, with 344 being differentially abundant. These 344 differentially abundant proteins were enriched in metabolic pathways and further examination revealed metabolic dysfunction consistent with the Warburg effect. Additionally, the protein data indicated activation of ESRRA and ESRRG, and HIF1A, as well as inhibition of FOXA1, MAPK1 and WISP2. A subset analysis of complementary gene expression array data on 47 pairs of these same tissues indicated similar upstream changes, such as increased HIF1A activation with stage, though ESRRA and ESRRG activation and FOXA1 inhibition were not predicted from the transcriptomic data. The activation of ESRRA and ESRRG implied that HIF2A may also be activated during later stages of ccRCC, which was confirmed in the transcriptional analysis. This combined analysis highlights the importance of HIF1A and HIF2A in developing the ccRCC molecular phenotype as well as the potential involvement of ESRRA and ESRRG in driving these changes. In addition, cofilin-1, profilin-1, nicotinamide N-methyltransferase, and fructose-bisphosphate aldolase A were identified as candidate markers of late stage ccRCC. Utilization of data collected from heterogeneous biological domains strengthened the findings from each domain, demonstrating the complementary nature of such an analysis. Together these results highlight the importance of the VHL/HIF1A/HIF2A axis and provide a foundation and therapeutic targets for future studies. (Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.).", "claims": [{"id": "claim-method", "statement": "Normalized protein spectral count data from patient matched tumor and normal-adjacent samples were used to calculate a tumor to normal-adjacent ratio for each protein, which was log2 transformed. These log2 transformed ratios were evaluated with a moderated t-test (limma package [34]; R v3.2.1) followed by a BH procedure to correct for multiple hypothesis testing.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:27128972", "locator": "sections[8] \"Data analysis\" paragraph 2", "quote": "Normalized protein spectral count data from patient matched tumor and normal-adjacent samples were used to calculate a tumor to normal-adjacent ratio for each protein, which was log2 transformed. These log2 transformed ratios were evaluated with a moderated t-test (limma package [34]; R v3.2.1) followed by a BH procedure to correct for multiple hypothesis testing."}], "state": "complete"}, {"id": "claim-data-access", "statement": "(Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.)", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:27128972", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "(Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.)"}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper used label-free proteomics with moderated t-tests and Benjamini–Hochberg correction for differential protein abundance, finding 1551 proteins (290 differentially abundant) in pooled samples and 783 proteins (344 differentially abundant) in individual samples; the source data are available through ProteomeXchange accession PXD003271 and MassIVE accession MSV000079511.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:27128972", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "To accomplish this, label-free proteomics was used to characterize matched tumor and normal-adjacent tissues from 84 patients with stage I to IV ccRCC."}, {"asset": "source:pmid:27128972", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Using pooled samples 1551 proteins were identified, of which 290 were differentially abundant, while 783 proteins were identified using individual samples, with 344 being differentially abundant."}, {"asset": "source:pmid:27128972", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "(Data are available via ProteomeXchange with identifier PXD003271 and MassIVE with identifier MSV000079511.)"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The paper used label-free proteomics with moderated t-tests and Benjamini–Hochberg correction for differential protein abundance, finding 1551 proteins (290 differentially abundant) in pooled samples and 783 proteins (344 differentially abundant) in individual samples; the source data are available through ProteomeXchange accession PXD003271 and MassIVE accession MSV000079511.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:27128972"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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